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Massively multi-label prediction/classification problems arise inenvironments like health-care or biology where very precise predictions areuseful. One challenge with massively multi-label problems is that there isoften a long-tailed frequency distribution for the labels, which results in fewpositive examples for the rare labels. We propose a solution to this problem bymodifying the output layer of a neural network to create a Bayesian network ofsigmoids which takes advantage of ontology relationships between the labels tohelp share information between the rare and the more common labels. We applythis method to the two massively multi-label tasks of disease prediction (ICD-9codes) and protein function prediction (Gene Ontology terms) and obtainsignificant improvements in per-label AUROC and average precision for lesscommon labels.

Using Ontologies To Improve Performance In Massively Multi-label Prediction Models